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How can you speed up data access and queries in data warehouses and lakes?

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Data warehouses and lakes are two common types of data storage systems that enable data analysis and reporting. However, they have different architectures, purposes, and challenges. In this article, you will learn some tips and techniques on how to speed up data access and queries in both data warehouses and lakes, and improve your data engineering performance and efficiency.

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Mandar Khoje

Senior Engineering Manager at Moveworks
Arif Alam

Making Data Science and AI Accessible to All | Creator | Educator | Storyteller | Building Data Science Reality
Vaibhav Mudgal

Data Engineer @PrecisionSports | LinkedIn Community Top Data Engineering Voice x1

1 Data warehouse basics

A data warehouse is a centralized, structured, and curated repository of data that is extracted, transformed, and loaded (ETL) from various sources. It supports fast and complex queries, aggregations, and calculations on large volumes of historical and current data. It also enables consistent and accurate reporting and analysis across different business units and functions.

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Arif Alam

Making Data Science and AI Accessible to All | Creator | Educator | Storyteller | Building Data Science Reality
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A data warehouse is like a well-organized library for an organization's data. It stores, cleans, and structures data from various sources, enabling fast and accurate analysis. It ensures consistency in reporting across the organization.

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2 Data lake basics

A data lake is a distributed, unstructured, and raw collection of data that is ingested from various sources without any transformation or schema. It supports diverse and flexible data types, formats, and processing methods. It also enables exploratory and ad-hoc analysis and machine learning on large volumes of real-time and historical data. It can also feed data into a data warehouse for further processing and refinement.

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Arif Alam

Making Data Science and AI Accessible to All | Creator | Educator | Storyteller | Building Data Science Reality
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A data lake is a raw, unstructured data reservoir, like an open field where data pours in from many sources without any initial organization. It's a versatile storage space, accommodating different data types and formats. Think of it as a playground for data exploration. It supports on-the-fly analysis and machine learning on vast amounts of real-time and historical data. Plus, it can act as a source, channeling data into a data warehouse for structured processing when needed.

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3 Data partitioning

One way to speed up data access and queries in both data warehouses and lakes is to partition the data into smaller and more manageable chunks based on some criteria, such as date, category, or region. Partitioning reduces the amount of data that needs to be scanned, filtered, or joined, and improves query performance and parallelism. For example, you can partition your sales data by month or by product line, and only query the relevant partitions for your analysis.

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Mandar Khoje

Senior Engineering Manager at Moveworks
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Choosing the right partitioning key is crucial, often determined by frequent query patterns. For instance, if your queries typically involve filtering by date or customer_id, it's recommended to partition the data using these criteria. An inappropriate partition key selection can lead to suboptimal performance, for example, a key with high cardinality will result in large number of small files.

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Arif Alam

Making Data Science and AI Accessible to All | Creator | Educator | Storyteller | Building Data Science Reality
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Data partitioning is like organizing a large library into sections by genre or author. It involves breaking down data into smaller, manageable chunks based on criteria like date, category, or region. By doing this, it's like having separate shelves for each genre in the library. When we need a specific book or data you know exactly where to look, saving time and effort. This organization boosts query speed because we only scan or search within the relevant partitions, enhancing performance and allowing for parallel processing. For instance, in sales data, we can partition by month or product line and focus on just the partitions we need for our analysis.

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4 Data indexing

Another way to speed up data access and queries in both data warehouses and lakes is to index the data based on some key columns or attributes that are frequently used in queries. Indexing creates a lookup table that maps the values of the key columns to the locations of the corresponding data rows or files. Indexing reduces the amount of data that needs to be read, sorted, or searched, and improves query performance and accuracy. For example, you can index your customer data by name or by ID, and quickly find the relevant records for your analysis.

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Vaibhav Mudgal

Data Engineer @PrecisionSports | LinkedIn Community Top Data Engineering Voice x1
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It's crucial to keep in mind that when a row is inserted or updated in the table, it can require the database system to reorganise or update the index data structure to maintain its integrity. Therefore, indexing should only be applied to columns that won't undergo future updates or changes.

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5 Data compression

A third way to speed up data access and queries in both data warehouses and lakes is to compress the data using some algorithm or format that reduces the size of the data without losing any information. Compression reduces the amount of data that needs to be stored, transferred, or loaded, and improves query performance and scalability. Compression also saves storage space and costs, and enhances data security and privacy. For example, you can compress your log data using gzip or snappy, and reduce the storage footprint and load time of your data.

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6 Data caching

A fourth way to speed up data access and queries in both data warehouses and lakes is to cache the data or the query results in memory or on disk for faster retrieval and reuse. Caching reduces the amount of data that needs to be computed, fetched, or refreshed, and improves query performance and responsiveness. Caching also enables faster iteration and experimentation with different queries and parameters. For example, you can cache your dashboard data or your frequent query results in a memory-based or disk-based cache, and avoid repeated computation and access of your data.

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7 Data format

A fifth way to speed up data access and queries in both data warehouses and lakes is to choose the right data format for your data based on your use case and requirements. Different data formats have different advantages and disadvantages in terms of performance, compatibility, and functionality. For example, CSV is a simple and widely used format, but it has no schema, compression, or metadata. Parquet is a columnar and compressed format, but it has more overhead and complexity. JSON is a flexible and nested format, but it has more verbosity and parsing costs. You should consider factors such as data size, structure, schema, compression, encoding, partitioning, indexing, and processing when choosing your data format.

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Mandar Khoje

Senior Engineering Manager at Moveworks
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In the context of enhancing performance that this article talks about, it's worth highlighting that for sizable datasets, Parquet typically outshines other formats like JSON and CSV. Despite its added overhead and complexity, the performance benefits offered by Parquet make these trade-offs justifiable.

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8 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you speed up data access and queries in data warehouses and lakes?

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1 Data warehouse basics

2 Data lake basics

3 Data partitioning

4 Data indexing

5 Data compression

6 Data caching

7 Data format

8 Here’s what else to consider

Data Engineering

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How can you speed up data access and queries in data warehouses and lakes?

1

2

3

4

5

6

7

8

1 Data warehouse basics

2 Data lake basics

3 Data partitioning

4 Data indexing

5 Data compression

6 Data caching

7 Data format

8 Here’s what else to consider

Data Engineering

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